Past, current, and future air-borne and space-borne imaging systems have the capability to acquire high spatial resolution panchromatic (pan) imagery along with lower spatial resolution, higher spectral resolution, multispectral imagery. Techniques have been developed to increase the spatial resolution of the multispectral data by merging the higher spatial resolution information of the pan image with the multispectral bands through a technique known as pan-sharpening. Pan-sharpening enhances the interpretability and the utility of the multispectral data. Pan-sharpening with a single high spatial resolution pan image also allows the multispectral bands to be acquired at an even lower spatial resolution. This is advantageous in that systems can be designed with lower bandwidth and storage requirements. Lower multispectral spatial resolution can also allow for increased spectral resolution when designing future imaging systems.
In the pan-sharpening process, a lower spatial resolution multispectral image is first registered (if necessary) to a higher spatial resolution pan image. Next, the registered multispectral image is merged with the pan image producing a multispectral image with higher spatial resolution. This merging process usually involves transforming the multispectral bands from the original multispectral band space into an alternative transform space. Image transformations retain all of the information present in the original multispectral bands and the transformed data is often more interpretable than the original data. Once the multispectral bands have been transformed into a transform space, the higher resolution pan image is swapped for (i.e. replaced with) the first transform band and the data is transformed back to original multispectral band space to yield the pan-sharpened multispectral data. Traditional ways of performing pan-sharpening have been shown to increase the spatial resolution of the multispectral data but they also have been shown to alter the spectral integrity of the data. Two widely used pan-sharpening techniques, as described in "Comparison of Three Different Methods to Merge Multiresolution and Multispectral Data", Chavez et al, Photogrammetric Engineering & Remote Sensing, March 1991, pages 295-303, are the Intensity, Hue, and Saturation (IHS) transform technique and the Principle Components (PC) transform technique.
The IHS transform technique is one of the most commonly used methods to pan-sharpen lower resolution multispectral data. In this method, a diagram of which is shown in FIG. 1, three of the total number (N) of low resolution multispectral bands 10 are transformed from original multispectral band space to IHS space 12. The mean digital count (.mu.) and standard deviation (.sigma.) of the intensity band (I) are then calculated 14 and the mean and standard deviation of the higher resolution pan image (P) 16 are also calculated 18. The higher resolution pan band is then modified 20 so that the mean and standard deviation of the image match the mean and standard deviation of the intensity band using the equation: EQU ModifiedP=(P*Gain)+Bias (1)
where ##EQU1## and EQU Bias=.mu.I-(Gain*.mu.P) (3)
This modification forces the global statistics of the two images to be similar in an attempt to preserve the spectral characteristics of the original multispectral data. The modified pan image is then swapped for the intensity band 22 and the data is back-transformed into original multispectral band space 24. This technique does improve the spatial resolution of the three lower resolution multispectral bands 26, however, there are two drawbacks to this technique. First, only three multispectral bands can be processed at one time. Hence, this method works well for color imagery which only have a red, green, and blue band, but most multispectral systems have more than three bands. This means that the process must be repeated for each three-band combination a user wishes to produce. Secondly, even though the global statistics of the higher resolution pan band and the intensity band are forced to be similar, the two images still may not look the same (i.e. they may not have similar local statistics). FIG. 2 shows the histograms (i.e. gray level distributions) of two images that visually are very different and yet both have the same global statistics (i.e. mean and standard deviation). This radiometric difference between the intensity band and the pan band may result in a change of spectral information when the IHS bands are back-transformed to original multispectral band space. Colors in certain areas of the pan-sharpened image may not match the colors of the original multispectral data.
The PC transform technique is another commonly used method for pan-sharpening lower resolution multispectral data. In this method, a diagram of which is shown in FIG. 3, all N of the lower resolution multispectral bands 30 are transformed from original multispectral band space into N PC bands 32. The mean and standard deviation of the first PC band (PC1) arc then calculated 34 and the mean and standard deviation of the higher resolution pan image 36 are also calculated 38. The higher resolution pan band is then modified 40 in one of two ways. The first way is to modify the pan band so that the mean digital count and standard deviation of the image match the mean and standard deviation of the first PC band using Equations 1-3. The second way is to modify the pan band so that the minimum and maximum digital counts match the minimum and maximum digital counts of the first PC band (i.e. force the data to the same digital count range). This modification again forces the global statistics of the two images to be similar in an attempt to preserve the spectral characteristics of the original multispectral data. The modified higher resolution pan image is then swapped for the first PC band 42, and the data is back-transformed into original multispectral band space 44. This technique has an advantage over the IHS technique in that it improves the spatial resolution of all N of the lower resolution multispectral bands 46 in one process. However, the radiometric accuracy of the first PC band is greater than the radiometric accuracy of the higher resolution pan band. This results in a loss of radiometric accuracy in the pan-sharpened image when the first PC band is replaced with the modified pan band. Also, even though the global statistics of the higher resolution pan band and the first PC band are similar, the two images may not look the same, so again they will not have similar local statistics. This radiometric difference between the first PC band and the pan band results in a change of spectral information when the PC bands are back-transformed to original multispectral band space. Again, the colors in certain areas of the pan-sharpened image may not match the colors of the original multispectral data.
A good pan-sharpening method should not only increase the spatial resolution of the multispectral data, but it should preserve the spectral integrity of the multispectral data as well. There is a need therefore for an improved pan-sharpening process that increases the spatial resolution of the multispectral imagery while preserving its spectral characteristics.